He, Yulan and Young, S.
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|DOI (Digital Object Identifier) Link:||http://doi.org/10.1109/ICASSP.2003.1198769|
|Google Scholar:||Look up in Google Scholar|
The paper presents a hidden vector state (HVS) model for hierarchical semantic parsing. The model associates each state of push-down automata with the state of an HMM. State transitions are factored into separate stack pop and push operations and then constrained to give a tractable search space. The result is a model which is complex enough to capture hierarchical structure but which can be trained automatically from unannotated data. Experiments have been conducted on ATIS-3 1993 and 1994 test sets. The results show that the HVS model outperforms a general finite state tagger (FST) by 19% to 32% in error reduction.
|Item Type:||Journal Article|
|Copyright Holders:||2003 IEEE|
|Keywords:||HMM; error reduction; general finite state tagger; hidden vector state model; hierarchical semantic parsing; push-down automata; search space; speech recognition; spoken dialogue systems; stack pop operations; stack push operations|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Interdisciplinary Research Centre:||Centre for Research in Computing (CRC)|
|Depositing User:||Kay Dave|
|Date Deposited:||29 Mar 2011 10:31|
|Last Modified:||05 Oct 2016 03:26|
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